code: def test_vectorize_simple():
      N = as_apply(15)
  
      p0 = hp_uniform("p0", 0, 1)
      loss = p0**2
      print(loss)
      expr_idxs = scope.range(N)
      vh = VectorizeHelper(loss, expr_idxs, build=True)
      vloss = vh.v_expr
  
      full_output = as_apply(
          [vloss, vh.idxs_by_label(), vh.vals_by_label()]
      )
      fo2 = replace_repeat_stochastic(full_output)
  
      new_vc = recursive_set_rng_kwarg(
          fo2, as_apply(np.random.default_rng(1))
      )
  
      # print new_vc
      losses, idxs, vals = rec_eval(new_vc)
      print("losses", losses)
      print("idxs p0", idxs["p0"])
      print("vals p0", vals["p0"])
      p0dct = dict(list(zip(idxs["p0"], vals["p0"])))
      for ii, li in enumerate(losses):
          assert p0dct[ii] ** 2 == li
  
file_path: hyperopt/hyperopt/tests/unit/test_vectorize.py
imports: import numpy
  from hyperopt.pyll import as_apply, scope, rec_eval, clone, dfs
  from hyperopt.pyll.stochastic import recursive_set_rng_kwarg
  from hyperopt import base, fmin, rand
  from hyperopt.vectorize import VectorizeHelper
  from hyperopt.vectorize import replace_repeat_stochastic
  from hyperopt.pyll_utils import hp_choice
  from hyperopt.pyll_utils import hp_uniform
  from hyperopt.pyll_utils import hp_quniform
  from hyperopt.pyll_utils import hp_loguniform
  from hyperopt.pyll_utils import hp_qloguniform
label_desc: Use image node_id 4 for calling a global function with example usage: test_vectorize_simple() without return types